The purpose of this work is to detect strong changes in spatial relationships between objects in video sequences,
with a limited knowledge on the objects. First, a fuzzy representation of the objects is proposed
based on low-level generic primitives. Furthermore, angle and distance histograms are used as examples to
model the spatial relationships between two objects. Then, we estimate the distances between different angle
or distance histograms during time. By analyzing the evolution of the spatial relationships during time, ruptures
are detected in this evolution. Experimental results show that the proposed method can efficiently detect
the ruptures in the spatial relationships, exploiting only low-level primitives. This constitutes a promising step
towards event detection in videos, with few a priori models on the objects.

Automatic face recognition has been integrated in many systems thanks to the improvement of face comparison algorithms. One of the main applications using facial biometry is the identity authentication at border control, which has already been adopted by a lot of airports. In order to proceed to a fast identity control, gates have been developed, to extract the ID document information on the one hand, and to acquire the facial information of the user on the other hand. The design of such gates, and in particular their camera configuration, has a high impact on the output acquisitions and therefore on the quality of the extracted facial features. Since it is very difficult to validate such gates by testing different configurations on real data in exactly the same conditions, we propose a validation protocol based on simulated passages. This method relies on synthetic sequences, which can be generated using any camera configuration with fixed parameters of identities and poses, and can also integrate different lighting conditions. We detail this methodology and present results in terms of geometrical error obtained with different camera configurations, illustrating the impact of the gate design on the 3D head fitting accuracy, and hence on facial authentication performances.

In this paper, we present a segmentation procedure based on a parametric active contour with shape constraint,
in order to follow the growth of the tomatoes from the images acquired in the field. This is a challenging task
because of the poor contrast in the images and the occlusions by the vegetation. In our sequential approach,
considering one image per day, we assume that a segmentation of the tomatoes is available for the image
acquired the previous day. An initial curve for the active contour model is computed by combining gradient
information and region information. Then, an active contour with shape constraint is applied to provide an
elliptic approximation of the tomato boundary. We performed a quantitative evaluation of our approach by
comparing the results with the manual segmentation. Given the varying degree of occlusion in the images, the
image data set was divided into three categories, based on the occlusion degree of the tomato in the processed
image. For the cases with low occlusion, good results were obtained, with an average relative distance between
the manual segmentation and the automatic segmentation of 2.73% (expressed as percentage of the size of
tomato). For the images with significant amount of occlusion, a good segmentation was obtained on 44% of
the images, where the average error was less than 10%.

We present a particle filter algorithm to optimize the static shape parameters of a given face observed under multiple views and during time. Our goal is to determine the 3D shape of the head given these observations, by selecting the most suitable deformation parameters. The main idea of our method is to integrate the unknown static parameters in the particle filter hidden state and to filter and modify these parameter values given the recursively incoming observations. We propose here a comparative study of different variants of this approach evaluated on synthetic data. These results show the potential given by this type of particle based methods,
which have mainly been presented from a theoretical point of view until now. We conclude with a discussion on the adaptation of these methods to real data sequences.

In this paper, we propose a novel method to track an object whose appearance is evolving in time. The tracking procedure is performed by a particle filter algorithm in which all possible appearance models are explicitly considered using a mixture decomposition of the likelihood. Then, the component weights of this mixture are conditioned by both the state and the current observation. Moreover, the use of the current observation makes the estimation process more robust and allows handling complementary features, such as color and shape information. In the proposed approach, these estimated component weights are computed using a Support Vector Machine. Tests on a mouth tracking problem show that the multiple appearance model outperforms classical single appearance likelihood.

In this paper we propose a new fuzzy segmentation method to segment lesions in Digital Breast Tomosynthesis (DBT) datasets. In the proposed approach we model a contour as a path in the image. The optimal contour is defined as the path associated with a minimal cost, which is derived from the image content. Using this formalism we present several ways to alter this cost in order to extract several relevant contours from a single image. The set of contours is then used in the fuzzy contour framework to perform mass detection. The method has been tested on synthetic data as well as images containing lesions and provides promising results.

In thoracic radiotherapy, some organs should be considered with care and protected from undesirable radiation. Among these organs, the heart is one of the most critical to protect. Its segmentation from routine CT scans provides valuable information to assess its position and shape. In this paper, we present a novel variational segmentation method for extracting the heart on non-contrast CT images. To handle the low image contrast around the cardiac borders, we propose to integrate shape constraints using Legendre moments and adding an energy term in the functional to be optimized. Results for whole heart segmentation in non-contrast CT images are presented and comparisons are performed with manual segmentations.

In this paper we propose to analyze the variability of brain structures using principal component analysis (PCA). We rely on a data base of registered and segmented 3D MRI images of normal subjects. We propose to use as input of PCA sampled points on the surface of the considered objects, selected using uniformity criteria or based on mean and Gaussian curvatures. Results are shown on the lateral ventricles. The main variation tendencies are observed in the orthogonal eigenvector space. Dimensionality reduction can be achieved and the variability of each landmark point is accurately described using the first three components.